Adaptive Signal Enhancement Based on Improved VMD-SVD for Leak Location in Water-Supply Pipeline

Abstract

The nonlinear non-stationary noise in surroundings leads to low signal to noise ratio (SNR) of leakage signal collected by acceleration sensors which generate larger leak location errors in water-supply pipelines. An adaptive signal enhancement based on genetic algorithm optimized variational mode decomposition (VMD) and singular value decomposition (SVD) is proposed for leak location in water-supply pipelines. Firstly, the fitness function was established as the optimization objective by minimum envelopment entropy of VMD modal component. Then the genetic algorithm was used for adaptive optimization of VMD parameters [ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> , K]. Secondly, the leakage signal is decomposed by adaptive-optimizing VMD into several intrinsic mode functions which can be selected by kurtosis analysis to reconstruct high-SNR (Signal to Noise Ratio) signal. Finally, Hankel matrix of the reconstructed signal is decomposed using SVD to further improve SNR. Simulation and experimental results show that the proposed signal enhancement is effective for reducing leak location errors. The SNR of the enhanced signal is improved by 94.27% compared with the initial signal and the average relative leak location error is reduced by 6.67 times using enhanced signal, which demonstrates feasibility and effectiveness of the proposed signal enhancement method.